Specifically, we format the problem of building a generally reusable model as an embodied set construction approach that constructs a domain invariant set structure using a large dataset covering the agent's vast task experiences. We call it an agent-environment interaction model since the set elements are discretized successor features [1]} which represent how a decision-making agent interacts with the world. We call our approach embodied set construction since the set structure is constructed so that it is a shared component across tasks and environments, which can be viewed as an embodiment of the agent. We call it generally reusable since learning by projecting onto the embodied set structure enables learning stability-plasticity. Our contributions are as follows:
